Watch material
MS&E 435 Stanford Economics of the AI Supercycle course
Reading material
- Daron Acemoglu — "The Simple Macroeconomics of AI" — Skeptical macro view on AI productivity gains
- Brynjolfsson, Li, Raymond — "Generative AI at Work" — Real empirical evidence of productivity gains from AI
- Stanford HAI — 2025 AI Index Report — Broad AI economy, investment, benchmarks, adoption data
- Goldman Sachs — "Gen AI: Too Much Spend, Too Little Benefit?" — Bubble/capex skepticism, AI ROI debate
- McKinsey — "The Cost of Compute: A $7 Trillion Race to Scale Data Centers" — AI infrastructure, data centers, compute economics
- IMF — "Gen-AI: Artificial Intelligence and the Future of Work" — Labor-market exposure, country-level AI impact
- Sequoia — "AI's $600B Question" by David Cahn — Revenue vs GPU/data-center spend; strongest VC bubble framing
- Epoch AI — "How much does it cost to train frontier AI models?" — Model training cost curves, compute scaling, frontier model economics
- Epoch AI — Trends in Artificial Intelligence — Data-rich dashboard on compute, training costs, model scaling
- NBER — "Firm Data on AI" — Firm-level AI adoption, executive usage, productivity signals